Model-based predictive control of office window shades

In the automation of interior window shading devices, a control system that relies on a prediction of environmental conditions and a building's thermal response can provide savings to space-conditioning loads beyond what can be achieved using a reactive approach. The development of these control strategies can be difficult because of the uniqueness of each building. A simplified model-based predictive control (MPC) method for window shades is proposed. To this end, a control-oriented model representing the heat transfer problem in a perimeter office space was developed. The parameters of the model were estimated using the ensemble Kalman filter (EnKF). The energy-savings potential of the EnKF-based MPC approach for window shades was investigated using EnergyPlus simulations. This was accomplished by implementing the control-oriented model into the energy management system application of EnergyPlus. Simulations were conducted to assess the energy saving potential of using the EnKF-based MPC for roller blinds in a south-facing perimeter office space in Ottawa, Canada. The simulation-based results indicate the potential for about 35% reduction in electricity usage for space conditioning over manually operated interior roller blinds.

[1]  H. B. Gunay,et al.  The Effect of Input Uncertainty in Model-based Predictive Control , 2014 .

[2]  A. D. Irving,et al.  Least squares parameter estimation of a reduced order thermal model of an experimental building , 1993 .

[3]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[4]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[5]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[6]  A. Athienitis,et al.  The impact of shading design and control on building cooling and lighting demand , 2007 .

[7]  Lino Guzzella,et al.  EKF based self-adaptive thermal model for a passive house , 2014 .

[8]  Dino Bouchlaghem,et al.  Estimating the energy consumption and power demand of small power equipment in office buildings , 2014 .

[9]  J. Beck,et al.  Bayesian State and Parameter Estimation of Uncertain Dynamical Systems , 2006 .

[10]  José A. Candanedo,et al.  Simplified Linear Models for Predictive Control of Advanced Solar Homes with Passive and Active Thermal Storage , 2010 .

[11]  Stephen Selkowitz,et al.  Thermal and daylighting performance of an automated venetian blind and lighting system in a full-scale private office , 1998 .

[12]  Balaji Rajagopalan,et al.  Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building , 2013 .

[13]  T. Y. Chen,et al.  Investigation of practical issues in building thermal parameter estimation , 2003 .

[14]  Frauke Oldewurtel,et al.  Building modeling as a crucial part for building predictive control , 2013 .

[15]  Candanedo Ibarra,et al.  Modelling and evaluation of the performance of building-integrated open loop air-based photovoltaic/thermal systems , 2010 .

[16]  Dino Bouchlaghem,et al.  Benchmarking small power energy consumption in office buildings in the United Kingdom: A review of data published in CIBSE Guide F , 2013 .

[17]  Christoph F. Reinhart,et al.  Lightswitch-2002: a model for manual and automated control of electric lighting and blinds , 2004 .

[18]  Gregor P. Henze,et al.  A model predictive control optimization environment for real-time commercial building application , 2013 .

[19]  Stephen Selkowitz,et al.  The New York Times headquarters daylighting mockup: Monitored performance of the daylighting control system , 2006 .

[20]  Ardeshir Mahdavi,et al.  A model-based approach to natural ventilation , 2008 .

[21]  Richard Perez,et al.  Forecasting solar radiation – Preliminary evaluation of an approach based upon the national forecast database , 2007 .

[22]  Athanasios Tzempelikos,et al.  Comparative control strategies for roller shades with respect to daylighting and energy performance , 2013 .

[23]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[24]  Andreas K. Athienitis,et al.  Manually-operated window shade patterns in office buildings: A critical review , 2013 .

[25]  Balaji Rajagopalan,et al.  Model-predictive control of mixed-mode buildings with rule extraction , 2011 .

[26]  Brandon Hencey,et al.  Online thermal estimation, control, and self-excitation of buildings , 2013, 52nd IEEE Conference on Decision and Control.

[27]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[28]  Brandon Hencey,et al.  Online building thermal parameter estimation via Unscented Kalman Filtering , 2012, 2012 American Control Conference (ACC).